he pervasive adoption of smartphones in contemporary society has raised sig nificant apprehensions concerning data privacy and security. These handheld devices, seamlessly integrated into the fabric of our daily lives, have evolved into reservoirs of personal information, thereby rendering them vulnerable to a spec trum of security risks and privacy breaches. This thesis comprehensively explores the intricate landscape of data privacy on smartphones, specifically emphasizing the challenges posed by Advanced Persistent Threats (APTs) and the escalating threat of URL phishing attacks, categorized as a subtype of APTs. By delv ing into the convergence of technological advancements, regulatory compliance, and proactive security measures, this thesis aims to offer a comprehensive and forward-thinking approach toward bolstering the protection of personal data on smartphones. The examination of these innovative deep learning-based detection systems marks a crucial contribution in addressing the evolving landscape of cy bersecurity threats, presenting a pathway to fortify smartphone users against sophisticated intrusion attempts and privacy breaches